{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os \n",
    "from os.path import  exists, join, split, isdir,isfile \n",
    "import glob\n",
    "import shutil\n",
    "\n",
    "\n",
    "import numpy as np \n",
    "\n",
    "\n",
    "\n",
    "# tgt_path = \"/data/xusc/exp/topictrack-bee/YOLOX_outputs/yolox_x_beedance_train_1026/train_log.txt\"\n",
    "# tgt_path = \"/data/xusc/exp/topictrack-bee/YOLOX_outputs/yolox_x_beedance_train_1030/train_log.txt\"\n",
    "# tgt_path = \"/data/xusc/exp/topictrack-bee/YOLOX_outputs/yolox_x_antmove_train_11_2/train_log.txt\"\n",
    "# tgt_path = \"/data/xusc/exp/topictrack-bee/YOLOX_outputs/yolox_x_antmove_train/train_log.txt\"\n",
    "# tgt_path = \"/data/xusc/exp/topictrack-bee/YOLOX_outputs/yolox_x_beedance_train/train_log.txt\"\n",
    "tgt_path = \"/data/xusc/exp/topictrack-bee/YOLOX_outputs/yolox_x_beedance_train/train_log.txt\"\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 用于提取loss信息的正则表达式模式\n",
    "loss_pattern = r\"total_loss: ([0-9.]+), iou_loss: ([0-9.]+), l1_loss: ([0-9.]+), conf_loss: ([0-9.]+), cls_loss: ([0-9.]+)\"\n",
    "\n",
    "# 读取txt文件\n",
    "with open(tgt_path, 'r') as file:\n",
    "    lines = file.readlines()\n",
    "\n",
    "# 初始化存储loss信息的列表\n",
    "total_loss = []\n",
    "iou_loss = []\n",
    "l1_loss = []\n",
    "conf_loss = []\n",
    "cls_loss = []\n",
    "\n",
    "# 遍历每一行，提取loss信息\n",
    "for line in lines:\n",
    "    match = re.search(loss_pattern, line)\n",
    "    if match:\n",
    "        total_loss.append(float(match.group(1)))\n",
    "        iou_loss.append(float(match.group(2)))\n",
    "        l1_loss.append(float(match.group(3)))\n",
    "        conf_loss.append(float(match.group(4)))\n",
    "        cls_loss.append(float(match.group(5)))\n",
    "\n",
    "# 绘制曲线图\n",
    "# 假设x轴为epoch数，可以根据实际情况修改\n",
    "epochs = range(1, len(total_loss) + 1)\n",
    "\n",
    "plt.plot(epochs, total_loss, label='total_loss')\n",
    "plt.plot(epochs, iou_loss, label='iou_loss')\n",
    "plt.plot(epochs, l1_loss, label='l1_loss')\n",
    "plt.plot(epochs, conf_loss, label='conf_loss')\n",
    "plt.plot(epochs, cls_loss, label='cls_loss')\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Loss Curve')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/data/xusc/exp/topictrack-bee/results/trackers/dancebee_test_post/data/bee02.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 20\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[39m# filename = '/data/xusc/exp/topictrack-bee/results/trackers/dancebee_test/data/bee02.txt'  # 替换为实际文件路径\u001b[39;00m\n\u001b[1;32m     19\u001b[0m filename \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m/data/xusc/exp/topictrack-bee/results/trackers/dancebee_test_post/data/bee02.txt\u001b[39m\u001b[39m'\u001b[39m  \u001b[39m# 替换为实际文件路径\u001b[39;00m\n\u001b[0;32m---> 20\u001b[0m result \u001b[39m=\u001b[39m check_consecutive_numbers(filename)\n\u001b[1;32m     21\u001b[0m \u001b[39mif\u001b[39;00m result:\n\u001b[1;32m     22\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39m第一列数字连续\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "Cell \u001b[0;32mIn[1], line 2\u001b[0m, in \u001b[0;36mcheck_consecutive_numbers\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcheck_consecutive_numbers\u001b[39m(filename):\n\u001b[0;32m----> 2\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(filename, \u001b[39m'\u001b[39;49m\u001b[39mr\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m file:\n\u001b[1;32m      3\u001b[0m         lines \u001b[39m=\u001b[39m file\u001b[39m.\u001b[39mreadlines()\n\u001b[1;32m      5\u001b[0m         \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(lines) \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m):\n",
      "File \u001b[0;32m~/miniconda3/envs/yang_real/lib/python3.8/site-packages/IPython/core/interactiveshell.py:282\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[39mif\u001b[39;00m file \u001b[39min\u001b[39;00m {\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m}:\n\u001b[1;32m    276\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    277\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mIPython won\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt let you open fd=\u001b[39m\u001b[39m{\u001b[39;00mfile\u001b[39m}\u001b[39;00m\u001b[39m by default \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    278\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    279\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39myou can use builtins\u001b[39m\u001b[39m'\u001b[39m\u001b[39m open.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    280\u001b[0m     )\n\u001b[0;32m--> 282\u001b[0m \u001b[39mreturn\u001b[39;00m io_open(file, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/data/xusc/exp/topictrack-bee/results/trackers/dancebee_test_post/data/bee02.txt'"
     ]
    }
   ],
   "source": [
    "def check_consecutive_numbers(filename):\n",
    "    with open(filename, 'r') as file:\n",
    "        lines = file.readlines()\n",
    "\n",
    "        for i in range(len(lines) - 1):\n",
    "            line1 = lines[i].strip().split(',')\n",
    "            line2 = lines[i + 1].strip().split(',')\n",
    "\n",
    "            num1 = int(line1[0])\n",
    "            num2 = int(line2[0])\n",
    "\n",
    "            if num2 - num1 > 1:\n",
    "                print(num2)\n",
    "                return False\n",
    "\n",
    "    return True\n",
    "\n",
    "# filename = '/data/xusc/exp/topictrack-bee/results/trackers/dancebee_test/data/bee02.txt'  # 替换为实际文件路径\n",
    "filename = '/data/xusc/exp/topictrack-bee/results/trackers/dancebee_test_post/data/bee02.txt'  # 替换为实际文件路径\n",
    "result = check_consecutive_numbers(filename)\n",
    "if result:\n",
    "    print(\"第一列数字连续\")\n",
    "else:\n",
    "    print(\"第一列数字不连续\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "图片宽度： 1280\n",
      "图片高度： 720\n"
     ]
    }
   ],
   "source": [
    "demo_img = '/data/xusc/exp/topictrack-bee/results/gt/beedance-val/bee02/img1/000003.jpg'\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "# 图片地址\n",
    "image_path = demo_img  # 替换为实际的图片路径\n",
    "\n",
    "# 打开图片\n",
    "image = Image.open(image_path)\n",
    "\n",
    "# 获取宽度和高度\n",
    "width, height = image.size\n",
    "\n",
    "# 打印宽度和高度\n",
    "print(\"图片宽度：\", width)\n",
    "print(\"图片高度：\", height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 \n",
    "\n",
    "a = cv2.imread('/data/xusc/exp/topictrack-bee/results/gt/antmove-val/ant04/img1/000001.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(720, 1280, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  }
 ],
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   "display_name": "yang_real",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
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 },
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